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Facilitating Cooperative and Distributed Multi-Vehicle Lane Change Maneuvers

Hansung Kim, Francesco Borrelli

TL;DR

This work tackles the challenge of coordinating cooperative lane changes among a small CAV platoon in the presence of uncertain human-driven vehicles. It introduces a facilitator CAV that proactively manipulates the environment to create feasible gaps, implemented via distributed MPC path-planners with three modes (Lane Keeping, Lane Change, Gap Regulation) governed by a higher-level FSM, and supported by a virtual-vehicle conflict-prevention framework. The approach leverages V2V information and NPC predictions to plan safe trajectories and manage interactions, demonstrating significantly higher lane-change feasibility in 200 dense traffic scenarios drawn from the NGSIM dataset, compared to a passive baseline. The results suggest that facilitator-driven coordination can enhance traffic throughput and safety for multi-CAV maneuvers in challenging traffic conditions, with future work on facilitator selection and experimental validation.

Abstract

A distributed coordination method for solving multi-vehicle lane changes for connected autonomous vehicles (CAVs) is presented. Existing approaches to multi-vehicle lane changes are passive and opportunistic as they are implemented only when the environment allows it. The novel approach of this paper relies on the role of a facilitator assigned to a CAV. The facilitator interacts with and modifies the environment to enable lane changes of other CAVs. Distributed MPC path planners and a distributed coordination algorithm are used to control the facilitator and other CAVs in a proactive and cooperative way. We demonstrate the effectiveness of the proposed approach through numerical simulations. In particular, we show enhanced feasibility of a multi-CAV lane change in comparison to the simultaneous multi-CAV lane change approach in various traffic conditions generated by using a data-set from real-traffic scenarios.

Facilitating Cooperative and Distributed Multi-Vehicle Lane Change Maneuvers

TL;DR

This work tackles the challenge of coordinating cooperative lane changes among a small CAV platoon in the presence of uncertain human-driven vehicles. It introduces a facilitator CAV that proactively manipulates the environment to create feasible gaps, implemented via distributed MPC path-planners with three modes (Lane Keeping, Lane Change, Gap Regulation) governed by a higher-level FSM, and supported by a virtual-vehicle conflict-prevention framework. The approach leverages V2V information and NPC predictions to plan safe trajectories and manage interactions, demonstrating significantly higher lane-change feasibility in 200 dense traffic scenarios drawn from the NGSIM dataset, compared to a passive baseline. The results suggest that facilitator-driven coordination can enhance traffic throughput and safety for multi-CAV maneuvers in challenging traffic conditions, with future work on facilitator selection and experimental validation.

Abstract

A distributed coordination method for solving multi-vehicle lane changes for connected autonomous vehicles (CAVs) is presented. Existing approaches to multi-vehicle lane changes are passive and opportunistic as they are implemented only when the environment allows it. The novel approach of this paper relies on the role of a facilitator assigned to a CAV. The facilitator interacts with and modifies the environment to enable lane changes of other CAVs. Distributed MPC path planners and a distributed coordination algorithm are used to control the facilitator and other CAVs in a proactive and cooperative way. We demonstrate the effectiveness of the proposed approach through numerical simulations. In particular, we show enhanced feasibility of a multi-CAV lane change in comparison to the simultaneous multi-CAV lane change approach in various traffic conditions generated by using a data-set from real-traffic scenarios.
Paper Structure (13 sections, 10 equations, 4 figures, 3 tables)

This paper contains 13 sections, 10 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Proactive and Cooperative multi-CAV platoon Lane Change Strategy: The Concept of Facilitator
  3. Vehicle Models
  4. Vehicle Dynamics
  5. NPC Prediction Model
  6. Distributed Coordination
  7. Finite State Machine
  8. MPC Path-planner
  9. Multi-CAV Conflict Prevention
  10. Numerical simulation
  11. NPC Controller
  12. NGSIM Dataset Simulation
  13. Conclusion

Figures (4)

  • Figure 1: Snapshots of the proactive and cooperative lane change strategy: (a) the facilitator change lane into the Target Lane (TL), (b) CAV 1 and CAV 2 accelerate while the facilitator decelerate to create free space for CAV 1 and CAV 2, (c) lane change for CAV 1 and CAV 2 is rendered feasible, (d) CAV 1 and CAV 2 completes a lane change
  • Figure 2: The kinematic bicycle model in Frenet Frame
  • Figure 3: Finite state machines of path planner modes in a highway driving scenario: (a) a FSM representing a passive and opportunistic lane change for a platoon P, (b) FSM with an additional mode---Gap Regulation---and its switching conditions listed in (c) for the proposed proactive and cooperative lane change strategy. Refer to Table \ref{['tab:FSM']} for definitions of the mode switching conditions.
  • Figure 4: Top: A multi-CAV platoon changes lanes using the proactive and cooperative lane change strategy in a dense traffic scenario. Note that orange vehicle 1, vehicle 2, and vehicle 3 correspond to CAV 1, CAV 2, and CAV 3, respectively. Scene (a) shows the initial scene of lane 2 and 3 at frame 561 from the NGSIM dataset. Scene (b)-(c) show the facilitator's (CAV 1) changing lanes and regulating the distance while CAV 2 and 3 get in the desired formation. Scene (d) shows the completion of platoon-wise lane change. Bottom: The multi-CAV platoon vehicle's states and inputs are shown. The labeled sections on the time axis correspond to the scenes described above.